STNGAN: GAN-Enhanced Style Transfer Network for Anime Sketch Colorization
Rongshen Hu, Bochao Chen, Xiaoqiang Li · Applied Sciences · 2026
Style transfer involves applying features from a stylized image to a content image, which has proven useful for image coloring. Significant progress has been made in utilizing neural networks for unsupervised sketch coloring using style transfer; however, existing models typically require user guidance. In this paper, we propose a GAN-Enhanced Style Transfer Network for Anime Sketch Colorization (STNGAN) that can fully automate coloring without user supervision. STNGAN incorporates a self-attention mechanism that enables the generator to capture global details and enhance color saturation and richness. The discriminator employs DenseNet to strengthen feature propagation and improve training stability. Additionally, we introduced a sketch reconstruction loss function to mitigate coloring overflow. Edge extraction was applied to obtain quantitative metrics. By contrasting light and dark areas between different color blocks and comparing them with the original sketches, we could objectively evaluate experimental performance using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics. Ablation experiments were conducted to assess the impact of self-attention and DenseNet on coloring. The results indicate that the proposed method achieved consistent improvements over the selected baselines under the evaluated anime sketch colorization setting. Quantitative experiments showed that STNGAN achieved a PSNR of 10.560, an SSIM of 0.768, and an inception score (IS) of 1.755. Compared with the strongest competing method, pix2pixHD, STNGAN improved the PSNR by 6.8%, the SSIM by 1.7%, and the IS by 8.0%. A user study with 50 participants further confirmed the perceptual advantage of STNGAN, which obtained the highest mean opinion score (MOS) of 4.129.